Comparison of Two Dispersion Models: A Bulk Petroleum Storage Terminal Case Study

Transcription

1 Comparison of Two Dispersion Models: A Bulk Petroleum Storage Terminal Case Study Prepared By: Anthony J. Schroeder BREEZE SOFTWARE Merit Drive Suite 900 Dallas, TX (972) breeze-software.com January 1, 2004 Modeling Software for EH&S Professionals

2 ABSTRACT In 65 FR (dated April 21, 2000), the United States Environmental Protection Agency (U.S. EPA) proposed revisions to its air dispersion modeling guidance, found in 40 CFR 51, Appendix W ( Guideline on Air Quality Models ) that included replacing the Industrial Source Complex (ISC) model with the American Meteorological Society (AMS) / EPA Regulatory Model (AERMOD) as the regulatory default model for state and federal permitting applications. It is expected that when this switch is finally promulgated, there will be an interim period during which results from either ISC or AERMOD will be considered acceptable. For this reason, it is both interesting and useful to explore differences in pollutant concentrations predicted by each model in a variety of industrial contexts. This study presents a comparison of the pollutant concentration predictions from the AERMOD and ISC (ISCST3 and ISC-PRIME) air dispersion models in the context of fugitive storage tank emissions at a bulk petroleum storage terminal. Data are presented that shows that ISC consistently predicts higher overall and higher maximum pollutant concentrations when compared with AERMOD in this particular situation. This trend is most pronounced when using a volume source to simulate fugitive tank emissions and least pronounced when using an area source. Predicated concentrations can vary for different facility configurations, in regions of differing terrain, and for different meteorological data sets. For this reason, this study should be viewed as an example of one application of these two dispersion models and not as a general treatment of predictions resulting from these models in all applications. INTRODUCTION In 65 FR (dated April 21, 2000), 1 the United States Environmental Protection Agency (U.S. EPA) proposed revisions to its air dispersion modeling guidance, found in 40 CFR 51, Appendix W ( Guideline on Air Quality Models ). One of the more farreaching revisions included is replacing the Industrial Source Complex (ISC) model with the American Meteorological Society (AMS) / EPA Regulatory Model (AERMOD) as the regulatory default model for state and federal permitting applications. It is expected that when this switch is finally promulgated, there will be an interim period during which results from either ISC or AERMOD will be considered acceptable. For this reason, it is 1A

3 both interesting and useful to explore differences in pollutant concentrations predicted by the models. The move in the regulatory community to replace ISC with a more advanced model was driven by the fact that the standard short-term version of ISC (ISCST3) has several theoretical deficiencies, including a poor characterization of building downwash and terrain features. AERMOD (along with the Plume Rise Model Enhancement (PRIME) algorithm) was developed in response to these deficiencies and is generally regarded as yielding a more theoretically accurate treatment of atmospheric dispersion. This does not necessarily mean that predicted concentrations from AERMOD will always be lower than those predicted by ISC, however. Previous studies have shown that predicted concentrations can vary for different source types and in regions of differing terrain complexity. At the same time that AERMOD was under development, refinements were made to ISC in an attempt to improve its deficiency regarding building downwash through the incorporation of the PRIME algorithm. Using ISC with the PRIME algorithm (referred to collectively as ISC-PRIME) generally results in an improved representation of building downwash effects. 2 On April 15, 2003, the majority of the proposed provisions of 65 FR were promulgated with the issuance of 68 FR The promulgation of AERMOD as the new regulatory default dispersion model was not included in this document, however. The general consensus of commenters on the promulgation of AERMOD was that a version of the PRIME downwash algorithm should be included with the regulatory default version of AERMOD in order to substantially differentiate it from ISC-PRIME. For this reason, U.S. EPA decided to temporarily postpone the promulgation of AERMOD. On September 8, 2003, U.S. EPA released a Notice of Data Availability in the Federal Register 4 in which it was announced that two new papers were added to the public docket that assess the performance of the most recent version of AERMOD, which includes the PRIME downwash algorithm. Also in this Notice of Data Availability, U.S. EPA states, it appears that the modified AERMOD is ready to be incorporated into the Guideline. At the time of the writing of this paper, there is every indication that AERMOD will be promulgated as the regulatory default short-range dispersion model within a matter of months or weeks. The following sections provide a brief overview of ISC and AERMOD, as well as the PRIME downwash algorithm. Next, a case study comparison of ISC- and AERMODpredicted 1-hour, 24-hour, and annual average pollutant concentrations associated with fugitive emissions from a large storage vessel at a bulk petroleum terminal will be presented. Emissions from storage vessels generally come from a series of vents near the top of the tank and around the top of its perimeter that allow product vapors to escape to the atmosphere at ambient temperatures and with no initial vertical velocity. This type of emission source may potentially be represented in both ISC and AERMOD in a number 2

4 of ways. The first manner in which this source is to be represented consists of a single emission point located at the top of the tank. The second consists of an area source that covers the top portion of the tank. Finally, the emission source may be represented as a volume source that is located near the top of the tank. The use of each of these source types results in the employment of differing dispersion algorithms in both ISC and AERMOD. Dispersion algorithms for the same source type also vary between the two models. For this reason, this case study will explore the differences in concentrations predicted by the two models using each of the source types listed above. It should once again be noted that previous studies have shown that predicted concentrations can vary for different source types and in regions of differing terrain complexity. For this reason, this study should be viewed as an example of one application of these two dispersion models and not as a general treatment of predictions resulting from these models in all applications. MODEL DESCRIPTIONS ISCST3 ISCST3 is a steady-state Gaussian plume dispersion model with a minimum one-hour time step that was developed specifically to support the U.S. EPA regulatory modeling programs. The concept of steady-state essentially means that for each hour of the modeled period, downwind concentrations are calculated as if the meteorological conditions are the same throughout the entire domain and have been the same for the entire hour. Due to its steady-state nature, ISCST3 is best used to predict pollutant concentrations within 50 kilometers of point, area, and volume sources. ISCST3 has been the workhorse of U.S. EPA regulatory models since it was first made available to the public in final form in early August In the following case study, modeling with ISCST3 is performed using the regulatory default option, which includes stack heights (for point sources) adjusted for stack-tip downwash, buoyancy-induced dispersion, and final plume rise. Ground-level concentrations occurring during calm wind conditions are calculated by the model using the calm processing feature. Regulatory default values for wind profile exponents and vertical potential temperature gradients are used since no representative on-site meteorological data are available. Rural dispersion coefficients are used in these cases. For the point sources runs, the PRIME downwash algorithm is used. Downwash is not calculated in the model algorithm used for area and volume sources, so the regulatory default version of ISCST3 is used in these runs (i.e., no PRIME downwash). Different algorithms are used by ISCST3 to compute atmospheric dispersion for the three different types of sources. Because of this, different versions of the ISCST3 program are run for each of the cases. In the point source runs, the version dated 01228, including the PRIME algorithm, is used. In both the area and volume source runs, the version of ISCST3 dated is used. 3

5 Throughout the remainder of this paper, both ISCST3 and ISC-PRIME will be referenced as ISC. AERMOD AERMOD is also a steady-state Gaussian plume dispersion model with a minimum onehour time step. It also has the ability to predict pollutant concentrations resulting from point, area, and volume sources and, as with ISC, due to its steady-state nature, it is best used to predict pollutant concentrations within 50 kilometers of the source. As in the ISC runs, modeling with AERMOD is performed using the regulatory default option in the following case study. The regulatory default option includes stack heights (for point sources) adjusted for stack-tip downwash and the use of the calm processing feature to predict ground-level concentrations during calm wind conditions. A major difference between ISC and AERMOD is seen in the simulation of boundary layer processes. Accurate simulation of boundary layer processes is important in dispersion modeling because this is the region of the atmosphere where most mixing and dispersion occurs. Whereas ISC uses relatively simple vertical profiles of wind and temperature gradients, AERMOD s treatment of the boundary layer is more complex (and more realistic). Surface land-use information is used in the AERMET data processor along with hourly meteorological data to produce more realistic profiles of parameters that affect boundary layer dispersion. As previously stated, the PRIME downwash algorithm has been incorporated into AERMOD and is used to predict downwash for the point sources runs. Once again, downwash is not calculated in the model algorithm used for area and volume sources, so the version of AERMOD without PRIME downwash is used in these runs. As with ISC, different versions of AERMOD are run for each of the source types. In the point source runs, the version dated is used along with the PRIME algorithm. In the area source runs, the version of AERMOD dated is used. Finally, in the volume source runs, the version dated is also used. More information and in-depth discussions on both ISC and AERMOD are available from U.S. EPA. 5, 6 MODEL INPUT DATA Emission Source and Tank Data Frequently, the effects of fugitive emissions from petroleum storage tanks are sought for regulatory purposes outside of terminal fencelines. In this case study, the effects of fugitive emissions of a generic pollutant (representing gasoline or any other petroleum product) from one tank at a petroleum storage terminal are studied. The tank is assumed to release one ton per year of fugitive emissions into the atmosphere. 4

6 Table 1 summarizes the source parameters used in the modeling analyses for each emissions source case. Table 1. Source Parameters Used in the ISC and AERMOD Modeling Analyses. CASE RELEASE HEIGHT (FT) STACK DIAMETER (M) EXIT VELOCITY (M/S) EXIT TEMP. ( F) SOURCE AREA (FT 2 ) INITIAL LATERAL DISPERSION COEFFICIENT DIMENSION (FT) INITIAL VERTICAL DISPERSION COEFFICIENT DIMENSION (FT) Point Source Case* Area Source Case Volume Source Case Ambient * The diameter, velocity, and temperature are set to these values in order to simulate a release with little or no plume rise. The location of the tank from which emissions are being analyzed is depicted in Figure 1. The relative location of the terminal fenceline, as well as the locations of other tanks located at the terminal, are also depicted in Figure 1. Structures located within close proximity to the emission source can contribute to downwash; for this reason, the accurate placement and simulation of structures near the emission source can dramatically affect modeled results. Receptor Grids and Terrain Three different grids of receptors are defined in order to provide a detailed mapping of ground level, off-property concentrations in the areas immediately surrounding the storage terminal. These grids cover a region extending one kilometer (km) from all edges of the facility fenceline. The first grid (boundary) contains 25-meter (m) spaced receptors along the fenceline. Next, a second grid (tight) contains 25-m spaced receptors extending approximately 100 m from the fenceline. Finally, a third grid (fine) contains 100-m spaced receptors extending approximately 1.0 km from the fenceline. In many regulatory cases, receptor grids must extend five or even 10 km from the facility fenceline. In almost all cases, however, the highest modeled concentrations are located within one km of the facility fenceline, so only this inner region is examined in this case study. The locations of the receptor grids relative to the facility are shown in Figure 2. 5

7 The receptor, source, and tank elevations input to the models are extracted from USGS 1:24,000 scale (7.5-minute series) topographical maps of the area surrounding the terminal. Elevations were determined electronically by processing Digital Elevation 6

8 Model (DEM) files published by USGS through the National Geospatial Data Clearinghouse. The elevations of the DEM points immediately surrounding each receptor are examined, with the highest values conservatively selected to represent the receptor, source, and tank elevations. In this case, simple terrain surrounds the terminal. Meteorological Data All runs are made with five consecutive years ( ) of meteorological data from a single measurement location. The meteorological data used in this study for both ISC and AERMOD runs consists of hourly surface and 12-hourly upper air observations taken at a nearby National Weather Service (NWS) site. 7

9 The meteorological data used in AERMOD runs is further processed using the AERMET preprocessor to incorporate land-use information into the AERMOD input files. The land within three km to the northeast and east of the terminal is used primarily for agricultural purposes. The land within three km to the south and west of the terminal is primarily used for urban housing purposes. As discussed in the Model Descriptions Section, landuse characteristics are taken into account for AERMOD to produce profiles of meteorological parameters in the atmospheric boundary layer, but not for ISC. ANALYSIS AND RESULTS Point Source Case Comparisons of ISC- and AERMOD-predicted concentrations for the point source case using the 1-hour, 24-hour, and annual averaging periods are shown in Figures 3, 4, and 5. Each data point plotted on these figures represents a comparison of ISC and AERMOD maximum modeled concentrations for the specified averaging period at a particular receptor. Each figure contains ISC versus AERMOD comparison points for each of the receptor grids (boundary, tight, and fine) and each of the meteorological data years ( ). In the runs using an emission point source, ISC predictions are generally higher than AERMOD predictions. This trend is especially evident in the 1-hour average cases (Figure 3) and the 24-hour average cases (Figure 4) where the majority of the comparison points fall to the right of the center line (the center line represents perfect agreement between the two models). For the annual averaging period, there is more agreement between ISC- and AERMOD-predicted concentrations. For the 1-hour and 24-hour averaging periods, as modeled concentrations increase, ISC-predicted concentrations become more consistently higher than AERMOD predictions. This point is particularly interesting in light of the fact that the highest modeled concentrations are most important in most regulatory modeling applications. 8

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11 The maximum concentrations (on any of the three grids and for any of the meteorological data years) for each model and averaging period in the point source case are presented in Table 2. This format presents a comparison of ISC and AERMOD results in a manner that is very relevant to regulatory applications. As stated previously, in many regulatory applications, the reviewing agency is only concerned with the highest predicted concentration anywhere on the grid and for any of the meteorological data years. As shown in the table, the maximum concentration predicted by ISC is higher than those predicted by AERMOD for all three averaging periods. These observations agree with those made using the comparison plots above. 10

12 Table 2. Maximum Predicted Pollutant Concentrations for the Point Source Case. AVERAGING PERIOD MODEL GRID YEAR MAXIMUM CONCENTRATION (µg/m 3 ) 1-Hour ISC Boundary Hour AERMOD Tight Hour ISC Tight Hour AERMOD Tight Annual ISC Boundary Annual AERMOD Boundary Area Source Case Comparisons of ISC- and AERMOD-predicted concentrations for the area source case using the 1-hour, 24-hour, and annual averaging periods are shown in Figures 6, 7, and 8. These figures contain data for the same grids and meteorological data years as the point source case. The results in the area source case are consistent with those seen in the point source case. Once again, ISC predictions are generally higher than AERMOD predictions, especially in the 1-hour average cases (Figure 6) and the 24-hour average cases (Figure 7) where 11

13 again, the majority of the comparison points fall to the right of the center line. For the annual averaging period, there is more agreement between ISC- and AERMOD-predicted concentrations. In the 24-hour and annual averaging period plots, the trend of consistent higher predictions by ISC is less evident for higher predicted concentrations. As was done in the point source case, the maximum concentration (on any of the three grids and for any of the meteorological data years) for each model and averaging period in the area source case are presented in Table 3. As shown in the table, the maximum concentrations predicted by ISC are higher than those predicted by AERMOD for the 1- and 24-hour averaging periods. In this case, however, the maximum annual average concentration is higher in the AERMOD runs than in the ISC runs. 12

14 These observations agree with those made for the area source case using the comparison plots above. While Figures 6 8 indicate that AERMOD predicts lower pollutant concentrations than ISC overall, the results in Table 3 indicate that where predictions of concentration are highest (and most important to the regulatory community), there is little difference in results between AERMOD and ISC for this area source case. Table 3. Maximum Predicted Pollutant Concentrations for the Area Source Case. AVERAGING PERIOD MODEL GRID YEAR MAXIMUM CONCENTRATION (µg/m 3 ) 1-Hour ISC Boundary Hour AERMOD Boundary Hour ISC Tight Hour AERMOD Boundary Annual ISC Boundary Annual AERMOD Boundary Volume Source Case Comparisons of ISC- and AERMOD-predicted concentrations for the volume source case using the 1-hour, 24-hour, and annual averaging periods are shown in Figures 9, 10, and 11. These figures contain data for the same grids and meteorological data years as in the point source and area source cases. The results in the volume source case are consistent with, but more pronounced than, those seen in the point and area source cases. Once again, ISC predictions are generally higher than AERMOD predictions, especially in the 1-hour average cases (Figure 9) and the 24-hour average cases (Figure 10) where in this case, nearly all of the comparison points fall to the right of the center line. For the annual averaging period, there is more agreement between ISC- and AERMOD-predicted concentrations for lower concentration predictions, but there is little agreement between the models for higher concentration predictions. For the 1-hour and 24-hour averaging periods, as modeled concentrations increase, ISC-predicted concentrations become more consistently higher than AERMOD predictions, as was the trend in the point source case. 13

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16 As was done in the point and area source cases, the maximum concentrations (on any of the grids and for any of the meteorological data years) for each model and averaging period in the volume source case are presented in Table 4. As shown in the table, the maximum concentrations predicted by ISC are considerably higher than those predicted by AERMOD for all three averaging periods. These observations agree with those made for the volume source case using the comparison plots above. Table 4. Maximum Predicted Pollutant Concentrations for the Volume Source Case. AVERAGING PERIOD MODEL GRID YEAR MAXIMUM CONCENTRATION (µg/m 3 ) 1-Hour ISC Boundary Hour AERMOD Boundary Hour ISC Boundary Hour AERMOD Tight Annual ISC Boundary Annual AERMOD Boundary

17 CONCLUSIONS The recent proposed and pending updates to the federal guideline for air quality modeling (40 CFR Part 51, Appendix W) include provisions through which AERMOD will likely replace ISC as the regulatory default air dispersion model for U.S. EPA regulatory purposes. It is important, therefore, to understand differences in predicted AERMOD and ISC pollutant concentrations for a variety of industrial facility types and also a variety of emission source types. A comparison of the pollutant concentration predictions from the AERMOD and ISC air dispersion models in the context of fugitive storage tank emissions at a bulk petroleum storage terminal in simple terrain is presented here. Data resulting from this study show that, in this context, ISC consistently predicts higher overall and higher maximum pollutant concentrations when compared with AERMOD. This trend is most pronounced using a volume source to simulate fugitive tank emissions and least pronounced using an area source. It should once again be noted that predicted concentrations could vary for different facility configurations, in regions of differing terrain, and for different meteorological data sets. For this reason, this study should be viewed as an example of one application of these two dispersion models and not as a general treatment of predictions resulting from these models in all applications. ACKNOWLEDGMENTS The author would like to thank Jeff DeToro, who served as a Trinity peer reviewer for this work. REFERENCES 1. Federal Register notice, 65 FR 21506, April 21, U.S. EPA, ``Comparison of Regulatory Design Concentrations: AERMOD vs. ISCST3, CTDMPLUS, ISD-PRIME.'' Office of Air Quality Planning and Standards, Research Triangle Park, NC 27711; EPA Report No. EPA-454/R , July Federal Register notice, 68 FR 18449, April 15, Federal Register notice, 68 FR 52934, September 8, U.S. EPA, User s Guide for the Industrial Source Complex (ISC3) Dispersion Models, Office of Air Quality Planning and Standards, Research Triangle Park, NC 27711, Report No. EPA-454/B a, September

18 6. U.S. EPA, AERMOD: Latest Features and Evaluation Results, Office of Air Quality Planning and Standards, Research Triangle Park, NC 27711, Report No. EPA-454/R , July